| Autor: |
Daamen AR; AMPEL BioSolutions LLC, Charlottesville, VA 22902, USA.; RILITE Research Institute, Charlottesville, VA 22902, USA., Bachali P; AMPEL BioSolutions LLC, Charlottesville, VA 22902, USA.; RILITE Research Institute, Charlottesville, VA 22902, USA., Grammer AC; AMPEL BioSolutions LLC, Charlottesville, VA 22902, USA.; RILITE Research Institute, Charlottesville, VA 22902, USA., Lipsky PE; AMPEL BioSolutions LLC, Charlottesville, VA 22902, USA.; RILITE Research Institute, Charlottesville, VA 22902, USA. |
| Abstrakt: |
The persistent impact of the COVID-19 pandemic and heterogeneity in disease manifestations point to a need for innovative approaches to identify drivers of immune pathology and predict whether infected patients will present with mild/moderate or severe disease. We have developed a novel iterative machine learning pipeline that utilizes gene enrichment profiles from blood transcriptome data to stratify COVID-19 patients based on disease severity and differentiate severe COVID cases from other patients with acute hypoxic respiratory failure. The pattern of gene module enrichment in COVID-19 patients overall reflected broad cellular expansion and metabolic dysfunction, whereas increased neutrophils, activated B cells, T-cell lymphopenia, and proinflammatory cytokine production were specific to severe COVID patients. Using this pipeline, we also identified small blood gene signatures indicative of COVID-19 diagnosis and severity that could be used as biomarker panels in the clinical setting. |
